161results about "Auscultation instruments" patented technology

The present application relates to the technical field of health monitoring, in particular to a kind of infant asphyxia risk autonomous monitoring method and system based on laryngeal respiratory sound, comprising: utilize high sensitivity MEMS microphone to collect the airflow and respiratory sound signal of infant larynx, and utilize micro accelerometer to monitor the thoracic movement data of infant synchronously;The airflow and respiratory sound signal are denoising, and the denoising respiratory sound signal is obtained;The denoising respiratory sound signal is separated according to respiratory event Signal, and is enhanced and combined into respiratory sound data set;Respiratory sound feature is extracted in respiratory sound data set, and asphyxia anomaly classification model is constructed based on respiratory sound feature by machine learning model, and dynamic baseline model that adapts individual respiratory difference is established according to respiratory sound feature.The present application utilizes the hardware device of high sensitivity MEMS microphone and micro accelerometer and artificial intelligence model to combine, realizes the use of laryngeal respiratory sound signal in home environment and autonomously completes infant asphyxia risk identification and early warning.

According to some embodiments, an ear-worn device, e.g., a hearing aid, is provided that operates to monitor the health of a wearer of the ear-worn device based on an audio signal detected by the ear-worn device. In some embodiments, a method of monitoring the health of the wearer of the ear-worn device includes: detecting the audio signal with a microphone of the ear-worn device; processing the detected audio signal using a processor of the ear-worn device; and outputting, from an output signal generator of the ear-worn device, a generated output audio signal. In some embodiments, processing the detected audio signal includes: extracting data indicative of a health event associated with the wearer by processing the detected audio signal with a machine learning model; generating the output audio signal based on the detected audio signal; and outputting the data indicative of the health event.

Disclosed are devices, systems and related methods for wearable devices that sense various anatomical conditions and / or metrics of a wearer, including measuring and optionally transmitting this data to a computing device which analyzes the data to determine various physiological conditions within and / or in proximity to a wearer's body. The device comprises a central body having a first sensor and a body piercing post with a second sensor, wherein the sensors measure a first and second physiological condition respectively of the wearer continuously or periodically using specified time intervals.

The invention discloses a multi-modal electrocardiosignal and arrhythmia recognition system and method. The system comprises a signal acquisition module, a preprocessing module, a space-time alignment module, a feature extraction module, a self-adaptive fusion module, a multi-modal fusion module, a classification risk assessment module and a visual report module. The recognition method comprises the following steps: S1, synchronously acquiring an electrocardiosignal and a heart sound signal, and preprocessing the electrocardiosignal and the heart sound signal; s2, establishing a time corresponding relation between the electrical activity and the mechanical activity; s3, calculating a mechanical shrinkage efficiency index; s4, evaluating the quality of the electrocardiosignal and the heart sound signal in real time, and dynamically adjusting contribution weights of the electrocardiosignal and the heart sound signal in a fusion process according to the quality; s5, adopting a multi-head attention mechanism to generate a joint feature vector for classification; s6, inputting the joint feature vector into a classification network model, and outputting an arrhythmia type and a quantitative risk level at the same time by the classification network model; and S7, generating a visual atlas and a structured diagnosis report.

A device for cardiac monitoring, comprising a processor having a digital signal processing unit, the digital signal processing unit configured to receive and process physiological signals from at least one sensor assembly having one or more sensors, the processor including a program having executable instructions configured, when executed on the processor, to cause the processor to perform the following steps: synchronizing the processed signals of each sensor assembly, mapping the synchronized signals as waveforms of each sensor, calculating at least one cardiac function parameter as data values ​​with predetermined waveform amplitudes and predefined time intervals, and performing a step of differential analysis between the calculated data values ​​and a set of reference cardiac health parameters to determine a cardiac condition.

An anti-snoring system comprising an adjustable bed having a sleeping surface that may be mechanically raised or lowered, and a control module adapted to receive commands from a source external to the adjustable bed; a mobile device in direct or indirect communication with the control module in the adjustable bed, and wherein the mobile device has sound recording capabilities; a mobile application resident on the mobile device, wherein the mobile application includes a sound classification machine learning model that includes an artificial intelligence or neural network operative to determine whether or not a person on the sleep surface is snoring, and wherein upon a determination that the person is snoring and has been snoring for a predetermined period of time, the mobile application instructs the control module to raise or adjust the sleeping surface to a height or position that will discourage the person from snoring.

A system is provided for detecting bruxism or a temporomandibular joint disorder. A pressure sensor senses an external ear canal air pressure and bruxism or a temporomandibular joint disorder are identified from the sensed ear canal air pressure. An output signal indicates the detected bruxism or temporomandibular joint disorders so that remedial action can be taken.

Reinforced surgical table mattresses or pads and methods of manufacture are disclosed. A layer of polymeric foam can be inserted into a shell formed from one or more sheets of flexible polymeric film material laminated to a fabric and welded together to create a water-proof shell. At least one layer of hotmelt glue film can be positioned inside the shell between the bottom side of the foam and the fabric of the shell. Heat can be applied to the outside of the shell at a temperature above the melting point of the hotmelt glue film and below that of the shell material, melting the glue film and bonding the fabric of the shell to an adjacent layer of material when cooled.

According to an aspect, an improved modular wearable medical device for monitoring a patient's ECG signals and providing therapeutic shocks in the event of a cardiac arrhythmia is provided. The device includes a garment, a plurality of ECG sensing electrodes that are permanently coupled at first predetermined locations on the garment, at least one pocket or receptacle disposed on the garment and configured to removably secure a module with device electronics within a housing to the garment, a plurality of wires or cables permanently integrated into the garment and operably coupling the plurality of permanently coupled ECG sensing electrodes with the sensor interface module, a plurality of therapy electrodes removably coupled to second predetermined locations on the garment, gel deployment circuitry, one or more capacitors, and a therapy control module configured to control the provision of the therapeutic shocks.

A device for heart monitoring, the device comprising a processor having a digital signal processing unit, wherein the digital signal processing unit is configured to receive and process physiological signals from at least one sensor assembly having one or more sensors. The processor comprises a program having executable instructions that when executed on the processor, the processor is configured to execute the steps of: synchronising processed signals for each sensor assembly; mapping the synchronised signals as waveforms for each sensor; predetermining waveform amplitudes and predetermined time intervals to calculate at least one cardiac function parameter as a data value; and performing a step of differential analysis between the calculated data value and a set of referencing cardiac health parameters to determine a condition of the heart.

The present invention discloses a method in a processor associated with a respiratory therapy device for identifying a component of an airpath coupled to the respiratory therapy device, the method comprising: processing a sound signal over a finite time window to obtain a cepstrum, the sound signal representing a sound in the airpath, wherein the finite time window is chosen to minimize a varying effect of a breathing cycle on acoustic characteristics of the airpath; separating an acoustic signature from the cepstrum; repeating the processing and separating at least once to generate a plurality of acoustic signatures with a plurality of finite time windows comprising the finite time window, combining the plurality of acoustic signatures into a combined acoustic signature; and identifying the component.

An in-ear device (IED) of a wearable system captures audio data from within the ear canal of a user indicative of the user's heartbeat. The IED when worn occludes the user's ear canal, causing amplification of low frequency sounds due to the occlusion effect, and improving the ability of the acoustic sensor to detect the user's heartbeat sounds. The wearable system classifies the audio data to identify portions of the audio data corresponding to a first heart sound and a second heart sound, which correspond to different portions of the heartbeat of the user. The wearable system estimates a blood pressure level of the user based upon the identified heart sounds.

Disclosed herein are systems and methods for providing feeding reinforcement in real-time or near real-time based on physiological sensor data acquired during feeding. In some examples, music reinforcement is rendered when one or more feeding features indicative of one or more feeding behaviors are detected using the physiological sensor data. When at least one feature is not detected, the music reinforcement is stopped. In this way, contingent reinforcement is provided in real-time or near real-time based on detection of the one or more feeding behaviors to encourage and improve independent feeding behavior.

A method (100) for obtaining a respiratory-related sound (RRS) (160) originating from a target patient is disclosed, the method comprising the steps of obtaining an input audio recording of a sleep environment of the target patient (110, 111), obtaining a respiratory trace of respiration of the target patient (150), identifying (120) RRSs (130) in the input audio recording, and selecting (140) the RRS (160) originating from the target patient from the RRSs based on the respiratory trace. The selection further comprises determining a first subset and / or a second subset of RRSs having a correspondingly high probability of originating from the target patient and / or a low probability of originating from the target patient, training a classifier based on the first subset and / or the second subset to select RRSs originating from the target patient, and selecting the RRS (160) originating from the target patient by the trained classifier.

The system, method, and apparatus includes a platform having a transparent material defining a first measurement surface. A light surface is disposed to provide light into the transparent material and generate a frustrated total internal reflection (FTIR) event in response to contact at the first measurement surface. The system also includes one or more wearable devices having a base material operable to cover a portion of a human body. An inner surface of the base material defines a second measurement surface. Further, one or more sensors disposed on the base material are operable to collect data at the second measurement surface. Further, the health parameter monitoring system presents displacement motion regimen information on a display screen, such as a virtual reality (VR) headset. The system collects first health parameter data from the first measurement surface based on the FTIR event; and / or collects second health parameter data from the second measurement surface.

An artificial intelligence-based system and method for scalable screening of individuals for respiratory infection, such as COVID-19. The system is trained to distinguish distinct latent features of cough sounds produced by a COVID-19 infected person from cough sounds produced by patients suffering from any other respiratory infection or involuntary cough sounds produced by a healthy person. Cough sound samples from individuals can be remotely collected and evaluated by the system for likelihood of the COVID-19 infection. Additionally, images of affected body parts, biomarkers, metadata, and other respiratory sound samples can also be used for screening.

A ventilation monitoring system for assisting in proper placement of an endotracheal tube in a subject includes: a capnography sensor configured to be placed in fluid communication with the endotracheal tube and to provide information representative of the subject's breath; and a processor in communication with the capnography sensor. The processor is configured to provide an indication of proper endotracheal tube placement when (1) a first indication of the subject's breath and a positive result of a first auscultation are identified within a first predetermined time period, and (2) a second indication of the subject's breath and a positive result of a second auscultation are identified within a second predetermined time period. The first auscultation includes auscultation of a subject's left lung, right lung, left axillary region, right axillary region, or abdomen. The second auscultation includes auscultation of another region of the subject different from the first auscultation.

The invention discloses a wearable intelligent sensing abdominal pain monitoring device and system, and belongs to the technical field of child medical health monitoring. The method comprises the steps that a monitoring range is constructed, symptom parameters are collected, and internal invisible symptom parameters and external invisible symptom parameters are generated; internal invisible symptom parameters are obtained, borborygmus core features are extracted, the intestinal tract abnormal reaction degree is obtained, and the intestinal tract abnormal reaction degree is subjected to score processing; according to related data acquired by the wearable intelligent sensing abdominal pain monitoring device, the device gets rid of dependence on subjective description of child patients completely on the basis of evaluation of physiological indexes such as borborygmus and abdominal muscle tension, is extremely high in adaptability to young children and children with language development retardation, can generate an evaluation report once in a short time, can dynamically track pain changes, and has a good application prospect. And the accuracy of pain grade judgment and disease cause pointing is improved through matching of a double-index fusion algorithm and a clinical database.

An ostomy bag can include one or more sensors for measuring one or more metrics. An ostomy wafer can also include one or more sensors for measuring one or more metrics. The sensors can be temperature sensors and / or capacitive sensors, for example, and the metrics can include bag fill, leakage, skin irritation, and phase of stoma output, among others.

Systems, methods, and apparatuses include a health parameter detection apparatus. The apparatus includes a platform having a measurement surface, the measurement surface being a first surface. The measurement surface is operable to contact a target area of a user. The apparatus includes one or more light sources disposed proximate to a transparent material and operable to transmit light into the transparent material. The apparatus further includes an internal mirror forming a first angle with the measurement surface and / or a camera such that the camera is operable to receive scattered light caused by frustrated total internal reflection (FTIR) occurring on the measurement surface and reflected from the mirror. The platform can form a portion of at least one of a treadmill, an elliptical machine, a rowing machine, a stair-stepper, or a leg press machine.

One or more sensors are configured to detect characteristics of moving objects and living subjects for human identification or verification. One or more processors, such as those in the sensor system or controlling the sensors, may be configured to process signals from the one or more sensors to identify a person. The processing may include estimating characteristics from the signals, such as respiratory rate, respiratory depth, degree of movement, and heart rate. The sensors may be radio frequency non-contact sensors with automatic detection control to change detection control parameters based on the identification of a living organism, thereby avoiding sensor interference.

A compliance-based cleaning device includes an internal housing, a sanitization chamber coupled to the internal housing and configured to receive a piece of equipment to be cleaned, and an ultraviolet lamp. The sanitization chamber transitions between an open position and a closed position by rotating about an axis within the internal housing. The ultraviolet lamp is located in proximity to the sanitization chamber and is configured to perform a cleaning process on the piece of equipment in response to the sanitization chamber entering the closed position.

This application provides a configuration method and apparatus for a heart sound processing model based on multimodal knowledge distillation. It introduces knowledge distillation technology on the basis of deep learning-based heart sound data processing and builds a novel model configuration scheme in a deep and adaptive manner. This realizes the integration of multimodal training and lightweight deployment of knowledge distillation, so as to comprehensively improve the accuracy, stability and engineering usability of diagnosis, and has good application prospects in clinical work.

The present application relates to the technical field of medical signal processing, in particular to an arrhythmia diagnosis system based on electrocardiogram signal feature analysis, comprising a heart sound signal acquisition module for real-time acquisition of patient heart sound signals; a data processing module for receiving the electrocardiogram signals and the heart sound signals, performing data processing to generate arrhythmia diagnosis results; an arrhythmia diagnosis module for arrhythmia type diagnosis; a Bluetooth module for transmitting the electrocardiogram signals, the heart sound signals and the arrhythmia diagnosis results to a mobile terminal. The present application overcomes the limitations of traditional fixed threshold methods under different physiological states by real-time acquisition of patient age, heart rate and basic disease information, dynamic calculation of synchronization threshold, so that the system can more accurately identify arrhythmia events in different populations, enhancing the individualized diagnosis and treatment ability of the system.

Systems, devices, and methods for facilitating diagnosis of sleep respiratory issues are provided. In one example, an ear device may be used as a home sleep test device to facilitate diagnosis of a sleep respiratory issue by, during a sleep session of a user, logging sleep data including multiple observations regarding existence or non-existence of an incident of breathing cessation, an indication regarding a current orientation of the head of the user, and potentially other data by performing repeated measurements over time of a positional sensor and potentially one or more other sensors associated with the ear device. Based on analysis of the logged sleep data by a cloud service, the user may be identified as potentially having a sleep respiratory issue and encouraged to consult with a medical professional, who may then make use of the captured sleep data to make a medical diagnosis.

A system is provided herein for stimulating an anatomical element of a patient. For example, a device may be configured to generate a current, and an electrode device coupled to the device may be configured to apply the current to the anatomical element. Additionally, the system may include a user interface in communication with the implantable pulse generator, the electrode device, or both. In some examples, the user interface may include a first element that is configured to display information associated with the patient. Additionally, the user interface may include a second element that is configured to receive inputs for programming parameters of the current. The user interface may also include a third element that is configured to display diagnostic information associated with applying the current to the anatomical element.

The present invention relates to a diagnostic system for determining a clinically relevant severity (S4) of a sleep-related breathing disturbance from a patient signal of a sleeping patient, comprising: a sensor unit (1) which captures a patient parameter and generates a patient signal (S1); a first detector unit (2) which detects respiratory events in the patient signal S1 and generates a corresponding respiratory event signal (S2) therefrom; and a second detector unit (3) which comprises the following: a weighting determination unit (3a) which detects, in the respiratory event signal (S2) at a particular respiratory event (REx) within a time window (ZFx) in which the respiratory event (REx) takes place, respiratory events (REx) that are close in time and, depending on a number and / or energy of the particular respiratory events (REx) that are close in time, generates a weighting signal (G); a weighting unit (3b) which weights the respiratory event signal (S2) with a weighting signal (G); and a severity determining unit (3c) which determines the clinically relevant severity (S4) from an integral via the weighted respiratory event signal (S3).

A method includes computing one or more values of at least one parameter at respective times during an exhalation of a subject (22), based on one or more properties of sound passing through air exhaled by the subject during the exhalation, the parameter being related to a concentration of a gas in the air. The method further includes generating (84) an output in response to the values. Other embodiments are also described.